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Artificial Intelligence-Based Applications for Bone Fracture Detection Using Medical Images: A Systematic Review.

Mohammed Kutbi¹

¹College of Computing and Informatics, Saudi Electronic University, Riyadh 13316, Saudi Arabia.

Diagnostics (Basel, Switzerland)

|September 14, 2024

Summary

Artificial intelligence (AI) significantly improves bone fracture detection using medical imaging. AI models like convolutional neural networks show superior accuracy, enhancing diagnostic capabilities and patient care.

Keywords:

bone fracture image classification medical images

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Area of Science:

Medical Imaging
Artificial Intelligence
Orthopedics

Background:

Bone fracture detection is critical in medical diagnostics.
Traditional methods face limitations in accuracy and efficiency.
AI presents a promising avenue for improving fracture diagnosis.

Purpose of the Study:

To systematically review AI applications for bone fracture detection in medical imaging.
To evaluate the performance of AI models in diagnosing bone fractures.
To explore the integration of advanced imaging techniques and AI.

Main Methods:

Systematic literature review of studies from 2010-2023.
Analysis of AI models, including convolutional neural networks (CNNs).
Assessment of AI integration with 3D CT and MRI.

Main Results:

AI models demonstrate superior accuracy, sensitivity, and specificity over traditional methods.
Integration with 3D CT and MRI enhances diagnostic accuracy.
Generative AI and LLMs show potential for synthetic data, reporting, and simulation.

Conclusions:

AI is transforming diagnostic workflows in bone fracture detection.
Enhanced accuracy and patient outcomes are key benefits.
Future research should focus on data quality, model robustness, and ethics.